junos
/
rapids
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Update file names

pull/103/head
JulioV 2020-10-19 15:07:12 -04:00
parent d32771fd9e
commit 24bf62a7ab
68 changed files with 495 additions and 669 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

218
Snakefile
View File

@ -13,17 +13,11 @@ if len(config["PIDS"]) == 0:
raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external")
if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: # valid sensed bins is necessary for sensed days, so we add these files anyways if sensed days are requested
if len(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) == 0:
raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml")
if len(config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]) == 0:
raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one PHONE_SENSOR to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml")
pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"]))
pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"]))
tables_android = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist
tables_ios = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist
for pids,table in zip([pids_android, pids_ios], [tables_android, tables_ios]):
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"])))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"])))
files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_timestamps.csv", pid=config["PIDS"]))
@ -33,106 +27,100 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]:
min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"],
min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"]))
for provider in config["MESSAGES"]["PROVIDERS"].keys():
if config["MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="MESSAGES".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="MESSAGES".lower()))
for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys():
if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_messages_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_messages_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"]))
for provider in config["CALLS"]["PROVIDERS"].keys():
if config["CALLS"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CALLS".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CALLS".lower()))
for provider in config["PHONE_CALLS"]["PROVIDERS"].keys():
if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["PHONE_CALLS"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"]))
for provider in config["BLUETOOTH"]["PROVIDERS"].keys():
if config["BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BLUETOOTH".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BLUETOOTH".lower()))
for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys():
if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"]))
for provider in config["ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
if config["ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]:
pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"]))
pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"]))
for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]):
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes_resampled.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="ACTIVITY_RECOGNITION".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="ACTIVITY_RECOGNITION".lower()))
for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"]))
for provider in config["BATTERY"]["PROVIDERS"].keys():
if config["BATTERY"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/battery_episodes.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/battery_episodes_resampled.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="BATTERY".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="BATTERY".lower()))
for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys():
if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_battery_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BATTERY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"]))
for provider in config["SCREEN"]["PROVIDERS"].keys():
if config["SCREEN"]["PROVIDERS"][provider]["COMPUTE"]:
if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]:
for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys():
if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]:
if "PHONE_SCREEN" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]:
files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]))
else:
raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)")
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/screen_episodes.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/screen_episodes_resampled.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="SCREEN".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="SCREEN".lower()))
raise ValueError("Error: Add PHONE_SCREEN (and as many phone sensor as you have in your database) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)")
files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"]))
for provider in config["LIGHT"]["PROVIDERS"].keys():
if config["LIGHT"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LIGHT".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LIGHT".lower()))
for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_light_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_light_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],))
for provider in config["ACCELEROMETER"]["PROVIDERS"].keys():
if config["ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="ACCELEROMETER".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="ACCELEROMETER".lower()))
for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys():
if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_accelerometer.csv", pid=config["PIDS"]))
for provider in config["APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys():
if config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="APPLICATIONS_FOREGROUND".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="APPLICATIONS_FOREGROUND".lower()))
for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys():
if config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"]))
for provider in config["WIFI"]["PROVIDERS"].keys():
if config["WIFI"]["PROVIDERS"][provider]["COMPUTE"]:
if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower()))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower()))
if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor_key}_with_datetime_visibleandconnected.csv", pid=config["PIDS"], sensor_key="WIFI".lower()))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["WIFI"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="WIFI".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="WIFI".lower()))
for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys():
if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"]))
for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys():
if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"]))
if config["HEARTRATE"]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"]))
@ -151,31 +139,27 @@ if config["SLEEP"]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"]))
files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"]))
for provider in config["CONVERSATION"]["PROVIDERS"].keys():
if config["CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]:
pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"]))
pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"]))
for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]):
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="CONVERSATION".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="CONVERSATION".lower()))
for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys():
if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime_unified.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_conversation.csv", pid=config["PIDS"]))
for provider in config["LOCATIONS"]["PROVIDERS"].keys():
if config["LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]:
if config["LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED":
if config["LOCATIONS"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]:
for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys():
if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]:
if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED":
if config["PHONE_LOCATIONS"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]:
files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]))
else:
raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)")
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor}_processed_{locations_to_use}.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor}_processed_{locations_to_use}_with_datetime.csv", pid=config["PIDS"], sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="LOCATIONS".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="LOCATIONS".lower()))
files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed_with_datetime.csv", pid=config["PIDS"]))
files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"]))
# visualization for data exploration
if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]:

129
config.yaml
View File

@ -32,9 +32,12 @@ READABLE_DATETIME:
PHONE_VALID_SENSED_BINS:
COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features
BIN_SIZE: &bin_size 5 # (in minutes)
# Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS.
# If you are extracting screen or Barnett's location features, screen and locations tables are mandatory.
DB_TABLES: []
# Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS.
# If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory.
# You can choose any of the keys shown below, just make sure its DB_TABLE exists in your database!
# PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT,
# PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION
PHONE_SENSORS: []
PHONE_VALID_SENSED_DAYS:
COMPUTE: False
@ -42,7 +45,7 @@ PHONE_VALID_SENSED_DAYS:
MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins)
# Communication SMS features config, TYPES and FEATURES keys need to match
MESSAGES:
PHONE_MESSAGES:
DB_TABLE: messages
PROVIDERS:
RAPIDS:
@ -52,10 +55,10 @@ MESSAGES:
received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
SRC_LANGUAGE: "r"
SRC_FOLDER: "rapids" # inside src/features/messages
SRC_FOLDER: "rapids" # inside src/features/phone_messages
# Communication call features config, TYPES and FEATURES keys need to match
CALLS:
PHONE_CALLS:
DB_TABLE: calls
PROVIDERS:
RAPIDS:
@ -66,20 +69,13 @@ CALLS:
incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
SRC_LANGUAGE: "r"
SRC_FOLDER: "rapids" # inside src/features/calls
SRC_FOLDER: "rapids" # inside src/features/phone_calls
APPLICATION_GENRES:
CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
UPDATE_CATALOGUE_FILE: false # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
SCRAPE_MISSING_GENRES: false # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
LOCATIONS:
PHONE_LOCATIONS:
DB_TABLE: locations
LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED
FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold
FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row
TIMEZONE: *timezone
PROVIDERS:
DORYAB:
COMPUTE: False
@ -90,7 +86,7 @@ LOCATIONS:
MAXIMUM_GAP_ALLOWED: 300
MINUTES_DATA_USED: False
SAMPLING_FREQUENCY: 0
SRC_FOLDER: "doryab" # inside src/features/locations
SRC_FOLDER: "doryab" # inside src/features/phone_locations
SRC_LANGUAGE: "python"
BARNETT:
@ -99,20 +95,20 @@ LOCATIONS:
ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius
TIMEZONE: *timezone
MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features
SRC_FOLDER: "barnett" # inside src/features/locations
SRC_FOLDER: "barnett" # inside src/features/phone_locations
SRC_LANGUAGE: "r"
BLUETOOTH:
PHONE_BLUETOOTH:
DB_TABLE: bluetooth
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
SRC_FOLDER: "rapids" # inside src/features/bluetooth
SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth
SRC_LANGUAGE: "r"
ACTIVITY_RECOGNITION:
PHONE_ACTIVITY_RECOGNITION:
DB_TABLE:
ANDROID: plugin_google_activity_recognition
IOS: plugin_ios_activity_recognition
@ -124,19 +120,19 @@ ACTIVITY_RECOGNITION:
STATIONARY: ["still", "tilting"]
MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
VEHICLE: ["in_vehicle"]
SRC_FOLDER: "rapids" # inside src/features/activity_recognition
SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition
SRC_LANGUAGE: "python"
BATTERY:
PHONE_BATTERY:
DB_TABLE: battery
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
SRC_FOLDER: "rapids" # inside src/features/battery
SRC_FOLDER: "rapids" # inside src/features/phone_battery
SRC_LANGUAGE: "python"
SCREEN:
PHONE_SCREEN:
DB_TABLE: screen
PROVIDERS:
RAPIDS:
@ -146,25 +142,25 @@ SCREEN:
IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable
FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later
EPISODE_TYPES: ["unlock"]
SRC_FOLDER: "rapids" # inside src/features/screen
SRC_FOLDER: "rapids" # inside src/features/phone_screen
SRC_LANGUAGE: "python"
LIGHT:
PHONE_LIGHT:
DB_TABLE: light
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
SRC_FOLDER: "rapids" # inside src/features/light
SRC_FOLDER: "rapids" # inside src/features/phone_light
SRC_LANGUAGE: "python"
ACCELEROMETER:
PHONE_ACCELEROMETER:
DB_TABLE: accelerometer
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
SRC_FOLDER: "rapids" # inside src/features/accelerometer
SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer
SRC_LANGUAGE: "python"
PANDA:
@ -173,11 +169,16 @@ ACCELEROMETER:
FEATURES:
exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"]
SRC_FOLDER: "panda" # inside src/features/accelerometer
SRC_FOLDER: "panda" # inside src/features/phone_accelerometer
SRC_LANGUAGE: "python"
APPLICATIONS_FOREGROUND:
PHONE_APPLICATIONS_FOREGROUND:
DB_TABLE: applications_foreground
APPLICATION_CATEGORIES:
CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway
PROVIDERS:
RAPIDS:
COMPUTE: False
@ -189,9 +190,45 @@ APPLICATIONS_FOREGROUND:
EXCLUDED_CATEGORIES: []
EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"]
FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
SRC_FOLDER: "rapids" # inside src/features/applications_foreground
SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground
SRC_LANGUAGE: "python"
PHONE_WIFI_VISIBLE:
DB_TABLE: "wifi"
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible
SRC_LANGUAGE: "r"
PHONE_WIFI_CONNECTED:
DB_TABLE: "sensor_wifi"
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected
SRC_LANGUAGE: "r"
PHONE_CONVERSATION:
DB_TABLE:
ANDROID: plugin_studentlife_audio_android
IOS: plugin_studentlife_audio
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
"sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy",
"avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction",
"voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
"unknownexpectedfraction","countconversation"]
RECORDING_MINUTES: 1
PAUSED_MINUTES : 3
SRC_FOLDER: "rapids" # inside src/features/phone_conversation
SRC_LANGUAGE: "python"
HEARTRATE:
COMPUTE: False
DB_TABLE: fitbit_data
@ -223,34 +260,6 @@ SLEEP:
SLEEP_TYPES: ["main", "nap", "all"]
SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"]
WIFI:
DB_TABLE:
VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to ""
CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to ""
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
SRC_FOLDER: "rapids" # inside src/features/bluetooth
SRC_LANGUAGE: "r"
CONVERSATION:
DB_TABLE:
ANDROID: plugin_studentlife_audio_android
IOS: plugin_studentlife_audio
PROVIDERS:
RAPIDS:
COMPUTE: False
FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration",
"sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy",
"avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction",
"voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction",
"unknownexpectedfraction","countconversation"]
RECORDING_MINUTES: 1
PAUSED_MINUTES : 3
SRC_FOLDER: "rapids" # inside src/features/conversation
SRC_LANGUAGE: "python"
### Visualizations ################################################################
HEATMAP_FEATURES_CORRELATIONS:
PLOT: False

51
rules/common.smk
View File

@ -14,69 +14,20 @@ def infer_participant_platform(participant_file):
return platform
# Preprocessing.smk ####################################################################################################
def optional_phone_sensed_bins_input(wildcards):
platform = infer_participant_platform("data/external/"+wildcards.pid)
if platform == "android":
tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist
elif platform == "ios":
tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist
return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform)
def optional_phone_sensed_timestamps_input(wildcards):
platform = infer_participant_platform("data/external/"+wildcards.pid)
if platform == "android":
tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist
elif platform == "ios":
tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist
return expand("data/raw/{{pid}}/{table}_raw.csv", table = tables_platform)
# Features.smk #########################################################################################################
def find_features_files(wildcards):
feature_files = []
for provider_key, provider in config[(wildcards.sensor_key).upper()]["PROVIDERS"].items():
if provider["COMPUTE"]:
feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=(wildcards.sensor_key).lower(), language=provider["SRC_LANGUAGE"].lower(), provider_key=provider_key))
feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=wildcards.sensor_key.lower(), language=provider["SRC_LANGUAGE"].lower(), provider_key=provider_key.lower()))
return(feature_files)
def optional_ar_input(wildcards):
platform = infer_participant_platform("data/external/"+wildcards.pid)
if platform == "android":
return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"])
elif platform == "ios":
return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"])
def optional_conversation_input(wildcards):
platform = infer_participant_platform("data/external/"+wildcards.pid)
if platform == "android":
return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CONVERSATION"]["DB_TABLE"]["ANDROID"])[0]
elif platform == "ios":
return expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CONVERSATION"]["DB_TABLE"]["IOS"])[0]
def optional_steps_sleep_input(wildcards):
if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED":
return "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv"
else:
return []
def optional_wifi_input(wildcards):
if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) == 0:
return {"visible_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])}
elif len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) == 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0:
return {"connected_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])}
elif len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0:
return {"visible_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]), "connected_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])}
else:
raise ValueError("If you are computing WIFI features you need to provide either VISIBLE_ACCESS_POINTS, CONNECTED_ACCESS_POINTS or both")
# Models.smk ###########################################################################################################
def input_merge_features_of_single_participant(wildcards):

302
rules/features.smk
View File

@ -28,341 +28,211 @@ rule resample_episodes_with_datetime:
script:
"../src/data/readable_datetime.R"
rule accelerometer_r_features:
rule phone_accelerometer_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"])[0],
sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "accelerometer"
sensor_key = "phone_accelerometer"
output:
"data/interim/{pid}/accelerometer_features/accelerometer_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule accelerometer_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "accelerometer"
output:
"data/interim/{pid}/accelerometer_features/accelerometer_python_{provider_key}.csv"
"data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule activity_recognition_episodes:
input:
optional_ar_input
sensor_data = "data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv"
output:
"data/interim/{pid}/activity_recognition_episodes.csv"
"data/interim/{pid}/phone_activity_recognition_episodes.csv"
script:
"../src/features/activity_recognition/episodes/activity_recognition_episodes.R"
"../src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R"
rule activity_recognition_r_features:
rule phone_activity_recognition_python_features:
input:
sensor_episodes = "data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv",
sensor_episodes = "data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "activity_recognition"
sensor_key = "phone_activity_recognition"
output:
"data/interim/{pid}/activity_recognition_features/activity_recognition_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule activity_recognition_python_features:
input:
sensor_episodes = "data/interim/{pid}/activity_recognition_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "activity_recognition"
output:
"data/interim/{pid}/activity_recognition_features/activity_recognition_python_{provider_key}.csv"
"data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule applications_foreground_r_features:
rule phone_applications_foreground_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])[0],
sensor_data = "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "applications_foreground"
sensor_key = "phone_applications_foreground"
output:
"data/interim/{pid}/applications_foreground_features/applications_foreground_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule applications_foreground_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "applications_foreground"
output:
"data/interim/{pid}/applications_foreground_features/applications_foreground_python_{provider_key}.csv"
"data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule battery_episodes:
input:
expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor=config["BATTERY"]["DB_TABLE"])
"data/raw/{pid}/phone_battery_raw.csv"
output:
"data/interim/{pid}/battery_episodes.csv"
"data/interim/{pid}/phone_battery_episodes.csv"
script:
"../src/features/battery/episodes/battery_episodes.R"
"../src/features/phone_battery/episodes/battery_episodes.R"
rule battery_r_features:
rule phone_battery_python_features:
input:
sensor_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv",
sensor_episodes = "data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_BATTERY"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "battery"
sensor_key = "phone_battery"
output:
"data/interim/{pid}/battery_features/battery_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule battery_python_features:
input:
sensor_episodes = "data/interim/{pid}/battery_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BATTERY"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "battery"
output:
"data/interim/{pid}/battery_features/battery_python_{provider_key}.csv"
"data/interim/{pid}/phone_battery_features/phone_battery_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule bluetooth_r_features:
rule phone_bluetooth_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])[0],
sensor_data = "data/raw/{pid}/phone_bluetooth_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_BLUETOOTH"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "bluetooth"
sensor_key = "phone_bluetooth"
output:
"data/interim/{pid}/bluetooth_features/bluetooth_r_{provider_key}.csv"
"data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule bluetooth_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["BLUETOOTH"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "bluetooth"
output:
"data/interim/{pid}/bluetooth_features/bluetooth_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule calls_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"])[0],
sensor_data = "data/raw/{pid}/phone_calls_with_datetime_unified.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "calls"
sensor_key = "phone_calls"
output:
"data/interim/{pid}/calls_features/calls_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule calls_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CALLS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "calls"
output:
"data/interim/{pid}/calls_features/calls_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule conversation_r_features:
input:
sensor_data = optional_conversation_input,
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "conversation"
output:
"data/interim/{pid}/conversation_features/conversation_r_{provider_key}.csv"
"data/interim/{pid}/phone_calls_features/phone_calls_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule conversation_python_features:
input:
sensor_data = optional_conversation_input,
sensor_data = "data/raw/{pid}/phone_conversation_with_datetime_unified.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["CONVERSATION"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_CONVERSATION"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "conversation"
sensor_key = "phone_conversation"
output:
"data/interim/{pid}/conversation_features/conversation_python_{provider_key}.csv"
"data/interim/{pid}/phone_conversation_features/phone_conversation_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule light_r_features:
rule phone_light_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"])[0],
sensor_data = "data/raw/{pid}/phone_light_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_LIGHT"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "light"
sensor_key = "phone_light"
output:
"data/interim/{pid}/light_features/light_r_{provider_key}.csv"
"data/interim/{pid}/phone_light_features/phone_light_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule phone_locations_r_features:
input:
sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "phone_locations"
output:
"data/interim/{pid}/phone_locations_features/phone_locations_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule light_python_features:
rule phone_locations_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"])[0],
sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LIGHT"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "light"
sensor_key = "phone_locations"
output:
"data/interim/{pid}/light_features/light_python_{provider_key}.csv"
"data/interim/{pid}/phone_locations_features/phone_locations_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule locations_r_features:
rule phone_messages_r_features:
input:
sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])[0],
sensor_data = "data/raw/{pid}/phone_messages_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_MESSAGES"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "locations"
sensor_key = "phone_messages"
output:
"data/interim/{pid}/locations_features/locations_r_{provider_key}.csv"
"data/interim/{pid}/phone_messages_features/phone_messages_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule locations_python_features:
input:
sensor_data = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["LOCATIONS"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "locations"
output:
"data/interim/{pid}/locations_features/locations_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule messages_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "messages"
output:
"data/interim/{pid}/messages_features/messages_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule messages_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"])[0],
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["MESSAGES"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "messages"
output:
"data/interim/{pid}/messages_features/messages_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule screen_episodes:
input:
screen = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["SCREEN"]["DB_TABLE"])
screen = "data/raw/{pid}/phone_screen_with_datetime_unified.csv"
output:
"data/interim/{pid}/screen_episodes.csv"
"data/interim/{pid}/phone_screen_episodes.csv"
script:
"../src/features/screen/episodes/screen_episodes.R"
"../src/features/phone_screen/episodes/screen_episodes.R"
rule screen_r_features:
rule phone_screen_python_features:
input:
sensor_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv",
sensor_episodes = "data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_SCREEN"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "screen"
sensor_key = "phone_screen"
output:
"data/interim/{pid}/screen_features/screen_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule screen_python_features:
input:
sensor_episodes = "data/interim/{pid}/screen_episodes_resampled_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["SCREEN"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}",
sensor_key = "screen"
output:
"data/interim/{pid}/screen_features/screen_python_{provider_key}.csv"
"data/interim/{pid}/phone_screen_features/phone_screen_python_{provider_key}.csv"
script:
"../src/features/entry.py"
rule wifi_r_features:
rule phone_wifi_connected_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower())[0],
sensor_data = "data/raw/{pid}/phone_wifi_connected_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "wifi"
sensor_key = "phone_wifi_connected"
output:
"data/interim/{pid}/wifi_features/wifi_r_{provider_key}.csv"
"data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_r_{provider_key}.csv"
script:
"../src/features/entry.R"
rule wifi_python_features:
rule phone_wifi_visible_r_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor_key}_with_datetime_visibleandconnected.csv", sensor_key="WIFI".lower())[0],
sensor_data = "data/raw/{pid}/phone_wifi_visible_with_datetime.csv",
day_segments_labels = "data/interim/day_segments/{pid}_day_segments_labels.csv"
params:
provider = lambda wildcards: config["WIFI"]["PROVIDERS"][wildcards.provider_key],
provider = lambda wildcards: config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][wildcards.provider_key.upper()],
provider_key = "{provider_key}",
sensor_key = "wifi"
sensor_key = "phone_wifi_visible"
output:
"data/interim/{pid}/wifi_features/wifi_python_{provider_key}.csv"
"data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_r_{provider_key}.csv"
script:
"../src/features/entry.py"
"../src/features/entry.R"
rule fitbit_heartrate_features:
input:

73
rules/preprocessing.smk
View File

@ -29,10 +29,10 @@ rule download_dataset:
"data/external/{pid}"
params:
group = config["DOWNLOAD_DATASET"]["GROUP"],
table = "{sensor}",
sensor = "{sensor}",
table = lambda wildcards: config[str(wildcards.sensor).upper()]["DB_TABLE"],
timezone = config["TIMEZONE"],
aware_multiplatform_tables = config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "," + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"] + "," + config["CONVERSATION"]["DB_TABLE"]["ANDROID"] + "," + config["CONVERSATION"]["DB_TABLE"]["IOS"],
unifiable_sensors = {"calls": config["CALLS"]["DB_TABLE"], "battery": config["BATTERY"]["DB_TABLE"], "screen": config["SCREEN"]["DB_TABLE"], "ios_activity_recognition": config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"], "ios_conversation": config["CONVERSATION"]["DB_TABLE"]["IOS"]}
aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["DB_TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["DB_TABLE"]["IOS"],
output:
"data/raw/{pid}/{sensor}_raw.csv"
script:
@ -50,35 +50,23 @@ rule compute_day_segments:
script:
"../src/data/compute_day_segments.py"
PHONE_SENSORS = []
PHONE_SENSORS.extend([config["MESSAGES"]["DB_TABLE"], config["CALLS"]["DB_TABLE"], config["LOCATIONS"]["DB_TABLE"], config["BLUETOOTH"]["DB_TABLE"], config["BATTERY"]["DB_TABLE"], config["SCREEN"]["DB_TABLE"], config["LIGHT"]["DB_TABLE"], config["ACCELEROMETER"]["DB_TABLE"], config["APPLICATIONS_FOREGROUND"]["DB_TABLE"], config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]])
PHONE_SENSORS.extend(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"])
if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0:
PHONE_SENSORS.append(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])
if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0:
PHONE_SENSORS.append(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])
rule readable_datetime:
rule phone_readable_datetime:
input:
sensor_input = "data/raw/{pid}/{sensor}_raw.csv",
sensor_input = "data/raw/{pid}/phone_{sensor}_raw.csv",
day_segments = "data/interim/day_segments/{pid}_day_segments.csv"
params:
timezones = None,
fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"],
day_segments_type = config["DAY_SEGMENTS"]["TYPE"],
include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
wildcard_constraints:
sensor = '(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ')' # only process smartphone sensors, not fitbit
output:
"data/raw/{pid}/{sensor}_with_datetime.csv"
"data/raw/{pid}/phone_{sensor}_with_datetime.csv"
script:
"../src/data/readable_datetime.R"
rule phone_sensed_bins:
input:
all_sensors = optional_phone_sensed_bins_input
all_sensors = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor = map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))
params:
bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"]
output:
@ -88,7 +76,7 @@ rule phone_sensed_bins:
rule phone_sensed_timestamps:
input:
all_sensors = optional_phone_sensed_timestamps_input
all_sensors = expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor = map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))
output:
"data/interim/{pid}/phone_sensed_timestamps.csv"
script:
@ -112,55 +100,50 @@ rule unify_ios_android:
participant_info = "data/external/{pid}"
params:
sensor = "{sensor}",
unifiable_sensors = {"calls": config["CALLS"]["DB_TABLE"], "battery": config["BATTERY"]["DB_TABLE"], "screen": config["SCREEN"]["DB_TABLE"], "ios_activity_recognition": config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"], "ios_conversation": config["CONVERSATION"]["DB_TABLE"]["IOS"]}
output:
"data/raw/{pid}/{sensor}_with_datetime_unified.csv"
script:
"../src/data/unify_ios_android.R"
rule process_location_types:
rule process_phone_location_types:
input:
locations = "data/raw/{pid}/{sensor}_raw.csv",
locations = "data/raw/{pid}/phone_locations_raw.csv",
phone_sensed_timestamps = "data/interim/{pid}/phone_sensed_timestamps.csv",
params:
consecutive_threshold = config["LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"],
time_since_valid_location = config["LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"],
locations_to_use = "{locations_to_use}"
wildcard_constraints:
locations_to_use = '(ALL|GPS|FUSED_RESAMPLED)'
consecutive_threshold = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"],
time_since_valid_location = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"],
locations_to_use = config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"]
output:
"data/interim/{pid}/{sensor}_processed_{locations_to_use}.csv"
"data/interim/{pid}/phone_locations_processed.csv"
script:
"../src/data/process_location_types.R"
rule readable_datetime_location_processed:
input:
sensor_input = expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"]),
sensor_input = "data/interim/{pid}/phone_locations_processed.csv",
day_segments = "data/interim/day_segments/{pid}_day_segments.csv"
params:
timezones = None,
fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"],
day_segments_type = config["DAY_SEGMENTS"]["TYPE"],
include_past_periodic_segments = config["DAY_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
wildcard_constraints:
locations_to_use = '(ALL|GPS|FUSED_RESAMPLED)'
output:
expand("data/interim/{{pid}}/{sensor}_processed_{locations_to_use}_with_datetime.csv", sensor=config["LOCATIONS"]["DB_TABLE"], locations_to_use=config["LOCATIONS"]["LOCATIONS_TO_USE"])
"data/interim/{pid}/phone_locations_processed_with_datetime.csv"
script:
"../src/data/readable_datetime.R"
rule application_genres:
rule phone_application_categories:
input:
"data/raw/{pid}/{sensor}_with_datetime.csv"
"data/raw/{pid}/phone_applications_foreground_with_datetime.csv"
params:
catalogue_source = config["APPLICATION_GENRES"]["CATALOGUE_SOURCE"],
catalogue_file = config["APPLICATION_GENRES"]["CATALOGUE_FILE"],
update_catalogue_file = config["APPLICATION_GENRES"]["UPDATE_CATALOGUE_FILE"],
scrape_missing_genres = config["APPLICATION_GENRES"]["SCRAPE_MISSING_GENRES"]
catalogue_source = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["CATALOGUE_SOURCE"],
catalogue_file = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["CATALOGUE_FILE"],
update_catalogue_file = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["UPDATE_CATALOGUE_FILE"],
scrape_missing_genres = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["SCRAPE_MISSING_CATEGORIES"]
output:
"data/raw/{pid}/{sensor}_with_datetime_with_genre.csv"
"data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv"
script:
"../src/data/application_genres.R"
"../src/data/application_categories.R"
rule fitbit_heartrate_with_datetime:
input:
@ -196,11 +179,3 @@ rule fitbit_sleep_with_datetime:
intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv"
script:
"../src/data/fitbit_readable_datetime.py"
rule join_wifi_tables:
input:
unpack(optional_wifi_input)
output:
"data/raw/{pid}/wifi_with_datetime_visibleandconnected.csv"
script:
"../src/data/join_visible_and_connected_wifi.R"

0
src/data/application_genres.R → src/data/application_categories.R
View File

191
src/data/assign_to_day_segment.R
View File

@ -2,31 +2,59 @@ library("tidyverse")
library("lubridate")
options(scipen=999)
find_segments_frequency <- function(local_date, local_time, local_timezone, segments){
assigned_segments <- segments[segments$segment_start<= local_time & segments$segment_end >= local_time, ]
assigned_segments["segment_start_ts"] = as.numeric(lubridate::as_datetime(stringi::stri_c(local_date,assigned_segments$segment_id_start_time), tz = local_timezone)) * 1000
assigned_segments["segment_end_ts"] = as.numeric(lubridate::as_datetime(stringi::stri_c(local_date,assigned_segments$segment_id_end_time), tz = local_timezone)) * 1000 + 999
return(stringi::stri_c(stringi::stri_c("[",
assigned_segments[["label"]], "#",
local_date, " ",
assigned_segments[["segment_id_start_time"]], ",",
local_date, " ",
assigned_segments[["segment_id_end_time"]], ";",
assigned_segments[["segment_start_ts"]], ",",
assigned_segments[["segment_end_ts"]],
"]"), collapse = "|"))
day_type_delay <- function(day_type, include_past_periodic_segments){
delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == day_type) %>% arrange(-length_duration) %>% pull(length_duration) %>% first()
return(if_else(is.na(delay) | include_past_periodic_segments == FALSE, duration("0days"), delay))
}
find_segments_periodic <- function(timestamp, segments){
# crossing and pivot_longer make segments a tibble, thus we need to extract [["segment_id"]]
return(stringi::stri_c(segments[[1]][segments[[1]]$segment_start_ts<= timestamp & segments[[1]]$segment_end_ts >= timestamp, "segment_id"][["segment_id"]], collapse = "|"))
get_segment_dates <- function(data, local_timezone, day_type, delay){
dates <- data %>%
distinct(local_date) %>%
mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>%
complete(local_date_obj = seq(date(min(local_date_obj) - delay), max(local_date_obj), by="days")) %>%
mutate(local_date = replace_na(as.character(date(local_date_obj))))
if(day_type == "every_day")
dates <- dates %>% mutate(every_day = 0)
else if (day_type == "wday")
dates <- dates %>% mutate(wday = wday(local_date_obj, week_start = 1))
else if (day_type == "mday")
dates <- dates %>% mutate(mday = mday(local_date_obj))
else if (day_type == "qday")
dates <- dates %>% mutate(qday = qday(local_date_obj))
else if (day_type == "yday")
dates <- dates %>% mutate(yday = yday(local_date_obj))
return(dates)
}
find_segments_event <- function(timestamp, segments){
# segments is a data.frame, we don't need to extract [["segment_id"]] like in find_segments_periodic
return(stringi::stri_c(segments[[1]][segments[[1]]$segment_start_ts<= timestamp & segments[[1]]$segment_end_ts >= timestamp, "segment_id"], collapse = "|"))
assign_rows_to_segments <- function(nested_data, nested_inferred_day_segments){
nested_data <- nested_data %>% mutate(assigned_segments = "")
for(i in 1:nrow(nested_inferred_day_segments)) {
segment <- nested_inferred_day_segments[i,]
nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$timestamp & segment$segment_end_ts >= nested_data$timestamp,
stringi::stri_c(nested_data$assigned_segments, segment$segment_id, sep = "|"), nested_data$assigned_segments)
}
nested_data$assigned_segments <- substring(nested_data$assigned_segments, 2)
return(nested_data)
}
assign_rows_to_segments_frequency <- function(nested_data, nested_timezone, day_segments){
for(i in 1:nrow(day_segments)) {
segment <- day_segments[i,]
nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$local_time_obj & segment$segment_end_ts >= nested_data$local_time_obj,
# The segment_id is assambled on the fly because it depends on each row's local_date and timezone
stringi::stri_c("[",
segment[["label"]], "#",
nested_data$local_date, " ",
segment[["segment_id_start_time"]], ",",
nested_data$local_date, " ",
segment[["segment_id_end_time"]], ";",
as.numeric(lubridate::as_datetime(stringi::stri_c(nested_data$local_date, segment$segment_id_start_time), tz = nested_timezone)) * 1000, ",",
as.numeric(lubridate::as_datetime(stringi::stri_c(nested_data$local_date, segment$segment_id_end_time), tz = nested_timezone)) * 1000 + 999,
"]"),
nested_data$assigned_segments)
}
return(nested_data)
}
assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type, include_past_periodic_segments){
@ -34,133 +62,102 @@ assign_to_day_segment <- function(sensor_data, day_segments, day_segments_type,
if(nrow(sensor_data) == 0)
return(sensor_data %>% mutate(assigned_segments = NA))
if(day_segments_type == "FREQUENCY"){ #FREQUENCY
if(day_segments_type == "FREQUENCY"){
day_segments <- day_segments %>% mutate(start_time = lubridate::hm(start_time),
end_time = start_time + minutes(length) - seconds(1),
segment_id_start_time = paste(str_pad(hour(start_time),2, pad="0"), str_pad(minute(start_time),2, pad="0"), str_pad(second(start_time),2, pad="0"),sep =":"),
segment_id_end_time = paste(str_pad(hour(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(minute(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(second(ymd("1970-01-01") + end_time),2, pad="0"),sep =":"), # add ymd("1970-01-01") to get a real time instead of duration
segment_start = as.numeric(start_time),
segment_end = as.numeric(end_time))
segment_start_ts = as.numeric(start_time),
segment_end_ts = as.numeric(end_time))
sensor_data <- sensor_data %>% mutate(local_time_obj = as.numeric(lubridate::hms(local_time)),
assigned_segments = pmap_chr(list(local_date, local_time_obj, local_timezone), find_segments_frequency, day_segments)) %>% select(-local_time_obj)
assigned_segments = "")
} else if (day_segments_type == "PERIODIC"){ #PERIODIC
sensor_data <- sensor_data %>%
group_by(local_timezone) %>%
nest() %>%
mutate(data = map2(data, local_timezone, assign_rows_to_segments_frequency, day_segments)) %>%
unnest(cols = data) %>%
arrange(timestamp) %>%
select(-local_time_obj)
return(sensor_data)
} else if (day_segments_type == "PERIODIC"){
# We need to take into account segment start dates that could include the first day of data
day_segments <- day_segments %>% mutate(length_duration = duration(length))
wday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "wday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first()
wday_delay <- if_else(is.na(wday_delay) | include_past_periodic_segments == FALSE, duration("0days"), wday_delay)
mday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "mday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first()
mday_delay <- if_else(is.na(mday_delay) | include_past_periodic_segments == FALSE, duration("0days"), mday_delay)
qday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "qday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first()
qday_delay <- if_else(is.na(qday_delay) | include_past_periodic_segments == FALSE, duration("0days"), qday_delay)
yday_delay <- day_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == "yday") %>% arrange(-length_duration) %>% pull(length_duration) %>% first()
yday_delay <- if_else(is.na(yday_delay) | include_past_periodic_segments == FALSE, duration("0days"), yday_delay)
every_day_delay <- duration("0days")
wday_delay <- day_type_delay("wday", include_past_periodic_segments)
mday_delay <- day_type_delay("mday", include_past_periodic_segments)
qday_delay <- day_type_delay("qday", include_past_periodic_segments)
yday_delay <- day_type_delay("yday", include_past_periodic_segments)
sensor_data <- sensor_data %>%
# mutate(row_n = row_number()) %>%
group_by(local_timezone) %>%
nest() %>%
# get existent days that we need to start segments from
mutate(every_date = map(data, ~.x %>%
distinct(local_date) %>%
mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>%
complete(local_date_obj = seq(min(local_date_obj), max(local_date_obj), by="days")) %>%
mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>%
mutate(every_day = 0)),
week_dates = map(data, ~.x %>%
distinct(local_date) %>%
mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>%
complete(local_date_obj = seq(date(min(local_date_obj) - wday_delay), max(local_date_obj), by="days")) %>%
mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>%
mutate(wday = wday(local_date_obj, week_start = 1)) ),
month_dates = map(data, ~.x %>%
distinct(local_date) %>%
mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>%
complete(local_date_obj = seq(date(min(local_date_obj) - mday_delay), max(local_date_obj), by="days")) %>%
mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>%
mutate(mday = mday(local_date_obj))),
quarter_dates = map(data, ~.x %>%
distinct(local_date) %>%
mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>%
complete(local_date_obj = seq(date(min(local_date_obj) - qday_delay), max(local_date_obj), by="days")) %>%
mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>%
mutate(qday = qday(local_date_obj)) ),
year_dates = map(data, ~.x %>%
distinct(local_date) %>%
mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>%
complete(local_date_obj = seq(date(min(local_date_obj) - yday_delay), max(local_date_obj), by="days")) %>%
mutate(local_date = replace_na(as.character(date(local_date_obj)))) %>%
mutate(yday = yday(local_date_obj)) ),
mutate(every_date = map2(data, local_timezone, get_segment_dates, "every_day", every_day_delay),
week_dates = map2(data, local_timezone, get_segment_dates, "wday", wday_delay),
month_dates = map2(data, local_timezone, get_segment_dates, "mday", mday_delay),
quarter_dates = map2(data, local_timezone, get_segment_dates, "qday", qday_delay),
year_dates = map2(data, local_timezone, get_segment_dates, "yday", yday_delay),
existent_dates = pmap(list(every_date, week_dates, month_dates, quarter_dates, year_dates),
function(every_date, week_dates, month_dates, quarter_dates, year_dates) reduce(list(every_date, week_dates,month_dates, quarter_dates, year_dates), .f=full_join)),
every_date = NULL,
week_dates = NULL,
month_dates = NULL,
quarter_dates = NULL,
year_dates = NULL,
# build the actual day segments taking into account the users requested leangth and repeat schedule
# build the actual day segments taking into account the users requested length and repeat schedule
inferred_day_segments = map(existent_dates,
~ crossing(day_segments, .x) %>%
pivot_longer(cols = c(every_day,wday, mday, qday, yday), names_to = "day_type", values_to = "day_value") %>%
filter(repeats_on == day_type & repeats_value == day_value) %>%
mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")), # The segment ids (label#start#end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones
# The segment ids (segment_id_start and segment_id_end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones
mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")),
segment_id_end = segment_id_start + lubridate::duration(length),
segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)) * 1000, # The actual segments are computed using timestamps taking into account the timezone
# The actual segments are computed using timestamps taking into account the timezone
segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)) * 1000,
segment_end_ts = segment_start_ts + as.numeric(lubridate::duration(length)) * 1000 + 999,
segment_id = paste0("[",
paste0(
label,"#",
paste0(label,"#",
paste0(lubridate::date(segment_id_start), " ",
paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",",
lubridate::date(segment_id_end), " ",
paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";",
paste0(segment_start_ts, ",", segment_end_ts)
),
paste0(segment_start_ts, ",", segment_end_ts)),
"]")) %>%
select(segment_start_ts, segment_end_ts, segment_id) %>%
drop_na(segment_start_ts, segment_end_ts)), # drop day segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 1am to 3am)
data = map2(data, inferred_day_segments, ~ .x %>% mutate(row_date_time = as.numeric(lubridate::ymd_hms(local_date_time, tz = local_timezone)) * 1000,
assigned_segments = map_chr(row_date_time, ~find_segments_periodic(.x, inferred_day_segments)),
row_date_time = NULL))
# drop day segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 01:59am to 03:00am)
drop_na(segment_start_ts, segment_end_ts)),
data = map2(data, inferred_day_segments, assign_rows_to_segments)
) %>%
select(-existent_dates, -inferred_day_segments) %>%
select(-existent_dates, -inferred_day_segments, -every_date, -week_dates, -month_dates, -quarter_dates, -year_dates) %>%
unnest(cols = data) %>%
arrange(timestamp)
} else if ( day_segments_type == "EVENT"){
sensor_data <- sensor_data %>%
group_by(local_timezone) %>%
nest() %>%
mutate(inferred_day_segments = map(local_timezone, ~ day_segments %>% mutate(shift = ifelse(shift == "0", "0seconds", shift),
mutate(inferred_day_segments = map(local_timezone, ~ day_segments %>%
mutate(shift = ifelse(shift == "0", "0seconds", shift),
segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000),
segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000),
segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = .x), # these start and end datetime objects are for labeling only
# these start and end datetime objects are for labeling only
segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = .x),
segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = .x),
segment_end_ts = segment_end_ts + 999,
segment_id = paste0("[",
paste0(
label,"#",
paste0(label,"#",
paste0(lubridate::date(segment_id_start), " ",
paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",",
lubridate::date(segment_id_end), " ",
paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";",
paste0(segment_start_ts, ",", segment_end_ts)
),
"]")) %>%
select(-segment_id_start, -segment_id_end)),
data = map2(data, inferred_day_segments, ~ .x %>% mutate(assigned_segments = map_chr(timestamp, ~find_segments_event(.x, inferred_day_segments))))) %>%
paste0(segment_start_ts, ",", segment_end_ts)),
"]"))),
data = map2(data, inferred_day_segments, assign_rows_to_segments)) %>%
select(-inferred_day_segments) %>%
unnest(data) %>%
arrange(timestamp)
}
return(sensor_data)

34
src/data/download_dataset.R
View File

@ -40,9 +40,9 @@ is_multiplaform_participant <- function(dbEngine, device_ids, platforms){
participant <- snakemake@input[[1]]
group <- snakemake@params[["group"]]
table <- snakemake@params[["table"]]
sensor <- snakemake@params[["sensor"]]
timezone <- snakemake@params[["timezone"]]
aware_multiplatform_tables <- str_split(snakemake@params[["aware_multiplatform_tables"]], ",")[[1]]
unifiable_tables = snakemake@params[["unifiable_sensors"]]
sensor_file <- snakemake@output[[1]]
device_ids <- strsplit(readLines(participant, n=1), ",")[[1]]
@ -58,30 +58,26 @@ end_datetime_utc = format(as.POSIXct(paste0(end_date, " 23:59:59"),format="%Y/%m
dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = group)
# Get existent columns in table
available_columns <- colnames(dbGetQuery(dbEngine, paste0("SELECT * FROM ", table, " LIMIT 1")))
if("device_id" %in% available_columns){
if(is_multiplaform_participant(dbEngine, device_ids, platforms)){
sensor_data <- unify_raw_data(dbEngine, table, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, unifiable_tables, device_ids, platforms)
}else {
if(is_multiplaform_participant(dbEngine, device_ids, platforms)){
sensor_data <- unify_raw_data(dbEngine, table, sensor, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, device_ids, platforms)
}else {
# table has two elements for conversation and activity recognition (they store data on a different table for ios and android)
if(length(table) > 1){
table <- table[[toupper(platforms[1])]]
}
query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')")
if("timestamp" %in% available_columns && !(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc)
if(!(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc)
query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')")
sensor_data <- dbGetQuery(dbEngine, query)
}
}
if("timestamp" %in% available_columns)
sensor_data <- sensor_data %>% arrange(timestamp)
sensor_data <- sensor_data %>% arrange(timestamp)
# Unify device_id
sensor_data <- sensor_data %>% mutate(device_id = unified_device_id)
# Unify device_id
sensor_data <- sensor_data %>% mutate(device_id = unified_device_id)
# Droping duplicates on all columns except for _id or id
sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id"))))
} else
stop(paste0("Table ", table, "does not have a device_id column (Aware ID) to link its data to a participant"))
# Droping duplicates on all columns except for _id or id
sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id"))))
write_csv(sensor_data, sensor_file)
dbDisconnect(dbEngine)

3
src/data/unify_ios_android.R
View File

@ -4,11 +4,10 @@ source("src/data/unify_utils.R")
sensor_data <- read.csv(snakemake@input[["sensor_data"]], stringsAsFactors = FALSE)
participant_info <- snakemake@input[["participant_info"]]
sensor <- snakemake@params[["sensor"]]
unifiable_sensors = snakemake@params[["unifiable_sensors"]]
platforms <- strsplit(readLines(participant_info, n=2)[[2]], ",")[[1]]
platform <- ifelse(platforms[1] == "multiple" | (length(platforms) > 1 & "android" %in% platforms & "ios" %in% platforms), "android", platforms[1])
sensor_data <- unify_data(sensor_data, sensor, platform, unifiable_sensors)
sensor_data <- unify_data(sensor_data, sensor, platform)
write.csv(sensor_data, snakemake@output[[1]], row.names = FALSE)

42
src/data/unify_utils.R
View File

@ -101,7 +101,7 @@ clean_ios_activity_column <- function(ios_gar){
return(ios_gar)
}
unify_ios_gar <- function(ios_gar){
unify_ios_activity_recognition <- function(ios_gar){
# We only need to unify Google Activity Recognition data for iOS
# discard rows where activities column is blank
ios_gar <- ios_gar[-which(ios_gar$activities == ""), ]
@ -138,7 +138,7 @@ unify_ios_conversation <- function(conversation){
}
# This function is used in download_dataset.R
unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, unifiable_tables, device_ids, platforms){
unify_raw_data <- function(dbEngine, sensor_table, sensor, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, device_ids, platforms){
# If platforms is 'multiple', fetch each device_id's platform from aware_device, otherwise, use those given by the user
if(length(platforms) == 1 && platforms == "multiple")
devices_platforms <- dbGetQuery(dbEngine, paste0("SELECT device_id,brand FROM aware_device WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')")) %>%
@ -147,8 +147,9 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc
devices_platforms <- data.frame(device_id = device_ids, platform = platforms)
# Get existent tables in database
available_tables_in_db <- dbGetQuery(dbEngine, paste0("SELECT table_name FROM information_schema.tables WHERE table_type = 'base table' AND table_schema='", dbGetInfo(dbEngine)$dbname,"'")) %>% pull(table_name)
available_tables_in_db <- dbGetQuery(dbEngine, paste0("SELECT table_name FROM information_schema.tables WHERE table_schema='", dbGetInfo(dbEngine)$dbname,"'"))[[1]]
if(!any(sensor_table %in% available_tables_in_db))
stop(paste0("You requested data from these table(s) ", paste0(sensor_table, collapse=", "), " but they don't exist in your database ", dbGetInfo(dbEngine)$dbname))
# Parse the table names for activity recognition and conversation plugins because they are different between android and ios
ar_tables <- setNames(aware_multiplatform_tables[1:2], c("android", "ios"))
conversation_tables <- setNames(aware_multiplatform_tables[3:4], c("android", "ios"))
@ -160,17 +161,19 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc
platform <- row$platform
# Handle special cases when tables for the same sensor have different names for Android and iOS (AR and conversation)
if(table %in% ar_tables)
if(length(sensor_table) == 1)
table <- sensor_table
else if(all(sensor_table == ar_tables))
table <- ar_tables[[platform]]
else if(table %in% conversation_tables)
else if(all(sensor_table == conversation_tables))
table <- conversation_tables[[platform]]
if(table %in% available_tables_in_db){
query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", device_id, "')")
if("timestamp" %in% available_columns && !(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc){
if(!(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc){
query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')")
}
sensor_data <- unify_data(dbGetQuery(dbEngine, query), table, platform, unifiable_tables)
sensor_data <- unify_data(dbGetQuery(dbEngine, query), sensor, platform)
participants_sensordata <- append(participants_sensordata, list(sensor_data))
}else{
warning(paste0("Missing ", table, " table. We unified the data from ", paste0(devices_platforms$device_id, collapse = " and "), " but without records from this missing table for ", device_id))
@ -182,25 +185,16 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc
}
# This function is used in unify_ios_android.R and unify_raw_data function
unify_data <- function(sensor_data, sensor, platform, unifiable_sensors){
if(sensor == unifiable_sensors$calls){
if(platform == "ios"){
unify_data <- function(sensor_data, sensor, platform){
if(sensor == "phone_calls" & platform == "ios"){
sensor_data = unify_ios_calls(sensor_data)
}
# android calls remain unchanged
} else if(sensor == unifiable_sensors$battery){
if(platform == "ios"){
} else if(sensor == "phone_battery" & platform == "ios"){
sensor_data = unify_ios_battery(sensor_data)
}
# android battery remains unchanged
} else if(sensor == unifiable_sensors$ios_activity_recognition){
sensor_data = unify_ios_gar(sensor_data)
} else if(sensor == unifiable_sensors$screen){
if(platform == "ios"){
} else if(sensor == "phone_activity_recognition" & platform == "ios"){
sensor_data = unify_ios_activity_recognition(sensor_data)
} else if(sensor == "phone_screen" & platform == "ios"){
sensor_data = unify_ios_screen(sensor_data)
}
# android screen remains unchanged
} else if(sensor == unifiable_sensors$ios_conversation){
} else if(sensor == "phone_conversation" & platform == "ios"){
sensor_data = unify_ios_conversation(sensor_data)
}
return(sensor_data)

0
src/features/accelerometer/panda/main.py → src/features/phone_accelerometer/panda/main.py
View File

0
src/features/accelerometer/rapids/main.py → src/features/phone_accelerometer/rapids/main.py
View File

0
src/features/activity_recognition/episodes/activity_recognition_episodes.R → src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R
View File

0
src/features/activity_recognition/rapids/main.py → src/features/phone_activity_recognition/rapids/main.py
View File

0
src/features/applications_foreground/rapids/main.py → src/features/phone_applications_foreground/rapids/main.py
View File

0
src/features/battery/episodes/battery_episodes.R → src/features/phone_battery/episodes/battery_episodes.R
View File

0
src/features/battery/rapids/main.py → src/features/phone_battery/rapids/main.py
View File

0
src/features/bluetooth/rapids/main.R → src/features/phone_bluetooth/rapids/main.R
View File

0
src/features/calls/rapids/main.R → src/features/phone_calls/rapids/main.R
View File

0
src/features/conversation/rapids/main.py → src/features/phone_conversation/rapids/main.py
View File

0
src/features/light/rapids/main.py → src/features/phone_light/rapids/main.py
View File

0
src/features/locations/barnett/library/AvgFlightDur.R → src/features/phone_locations/barnett/library/AvgFlightDur.R
View File

0
src/features/locations/barnett/library/AvgFlightLen.R → src/features/phone_locations/barnett/library/AvgFlightLen.R
View File

0
src/features/locations/barnett/library/Collapse2Pause.R → src/features/phone_locations/barnett/library/Collapse2Pause.R
View File

0
src/features/locations/barnett/library/DailyMobilityPlots.R → src/features/phone_locations/barnett/library/DailyMobilityPlots.R
View File

0
src/features/locations/barnett/library/DailyRoutineIndex.R → src/features/phone_locations/barnett/library/DailyRoutineIndex.R
View File

0
src/features/locations/barnett/library/DayDist.R → src/features/phone_locations/barnett/library/DayDist.R
View File

0
src/features/locations/barnett/library/DistanceTravelled.R → src/features/phone_locations/barnett/library/DistanceTravelled.R
View File

0
src/features/locations/barnett/library/ExtractFlights.R → src/features/phone_locations/barnett/library/ExtractFlights.R
View File

0
src/features/locations/barnett/library/ExtractTimePeriod.R → src/features/phone_locations/barnett/library/ExtractTimePeriod.R
View File

0
src/features/locations/barnett/library/GPS2MobMat.R → src/features/phone_locations/barnett/library/GPS2MobMat.R
View File

0
src/features/locations/barnett/library/GPSmobility-internal.R → src/features/phone_locations/barnett/library/GPSmobility-internal.R
View File

0
src/features/locations/barnett/library/GetMobilityFeaturesMat.R → src/features/phone_locations/barnett/library/GetMobilityFeaturesMat.R
View File

0
src/features/locations/barnett/library/GuessPause.R → src/features/phone_locations/barnett/library/GuessPause.R
View File

0
src/features/locations/barnett/library/Hometime.R → src/features/phone_locations/barnett/library/Hometime.R
View File

0
src/features/locations/barnett/library/InitializeParams.R → src/features/phone_locations/barnett/library/InitializeParams.R
View File

0
src/features/locations/barnett/library/IsFlight.R → src/features/phone_locations/barnett/library/IsFlight.R
View File

0
src/features/locations/barnett/library/LatLong2XY.R → src/features/phone_locations/barnett/library/LatLong2XY.R
View File

0
src/features/locations/barnett/library/LocationAt.R → src/features/phone_locations/barnett/library/LocationAt.R
View File

0
src/features/locations/barnett/library/MaxDiam.R → src/features/phone_locations/barnett/library/MaxDiam.R
View File

0
src/features/locations/barnett/library/MaxDistBetweenTrajectories.R → src/features/phone_locations/barnett/library/MaxDistBetweenTrajectories.R
View File

0
src/features/locations/barnett/library/MaxHomeDist.R → src/features/phone_locations/barnett/library/MaxHomeDist.R
View File

0
src/features/locations/barnett/library/MaxRadius.R → src/features/phone_locations/barnett/library/MaxRadius.R
View File

0
src/features/locations/barnett/library/MinsMissing.R → src/features/phone_locations/barnett/library/MinsMissing.R
View File

0
src/features/locations/barnett/library/MobilityFeatures.R → src/features/phone_locations/barnett/library/MobilityFeatures.R
View File

0
src/features/locations/barnett/library/MobmatQualityOK.R → src/features/phone_locations/barnett/library/MobmatQualityOK.R
View File

0
src/features/locations/barnett/library/ProbPause.R → src/features/phone_locations/barnett/library/ProbPause.R
View File

0
src/features/locations/barnett/library/ProgressBar.R → src/features/phone_locations/barnett/library/ProgressBar.R
View File

0
src/features/locations/barnett/library/RadiusOfGyration.R → src/features/phone_locations/barnett/library/RadiusOfGyration.R
View File

0
src/features/locations/barnett/library/RandomBridge.R → src/features/phone_locations/barnett/library/RandomBridge.R
View File

0
src/features/locations/barnett/library/SigLocEntropy.R → src/features/phone_locations/barnett/library/SigLocEntropy.R
View File

0
src/features/locations/barnett/library/SigLocs.R → src/features/phone_locations/barnett/library/SigLocs.R
View File

0
src/features/locations/barnett/library/SigLocsVisited.R → src/features/phone_locations/barnett/library/SigLocsVisited.R
View File

0
src/features/locations/barnett/library/SimulateMobilityGaps.R → src/features/phone_locations/barnett/library/SimulateMobilityGaps.R
View File

0
src/features/locations/barnett/library/StdFlightDur.R → src/features/phone_locations/barnett/library/StdFlightDur.R
View File

0
src/features/locations/barnett/library/StdFlightLen.R → src/features/phone_locations/barnett/library/StdFlightLen.R
View File

0
src/features/locations/barnett/library/WriteSurveyAnswers2File.R → src/features/phone_locations/barnett/library/WriteSurveyAnswers2File.R
View File

0
src/features/locations/barnett/library/plot.flights.R → src/features/phone_locations/barnett/library/plot.flights.R
View File

0
src/features/locations/barnett/library/plotlimits.R → src/features/phone_locations/barnett/library/plotlimits.R
View File

13
src/features/locations/barnett/main.R → src/features/phone_locations/barnett/main.R
View File

@ -3,7 +3,7 @@ library("dplyr")
library("stringr")
# Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility
file.sources = list.files(c("src/features/locations/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE)
file.sources = list.files(c("src/features/phone_locations/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE)
sapply(file.sources,source,.GlobalEnv)
create_empty_file <- function(requested_features){
@ -52,10 +52,13 @@ barnett_features <- function(sensor_data_files, day_segment, params){
if (nrow(location) > 1){
# Filter by segment and skipping any non-daily segment
location <- location %>% filter_data_by_segment(day_segment)
segment <- location %>% head(1) %>% pull(local_segment)
segment_data <- str_split(segment, "#")[[1]]
if(segment_data[[2]] != segment_data[[4]] || segment_data[[3]] != "00:00:00" || segment_data[[5]] != "23:59:59"){
warning(paste("Barnett's location features cannot be computed for day segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping for ", segment))
datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
location <- location %>% mutate(is_daily = str_detect(local_segment, paste0(day_segment, "#", datetime_start_regex, ",", datetime_end_regex)))
if(!all(location$is_daily)){
message(paste("Barnett's location features cannot be computed for day segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping ", day_segment))
location_features <- create_empty_file(requested_features)
} else {
# Count how many minutes of data we use to get location features

0
src/features/locations/doryab/main.py → src/features/phone_locations/doryab/main.py
View File

0
src/features/messages/rapids/main.R → src/features/phone_messages/rapids/main.R
View File

0
src/features/screen/episodes/screen_episodes.R → src/features/phone_screen/episodes/screen_episodes.R
View File

0
src/features/screen/rapids/main.py → src/features/phone_screen/rapids/main.py
View File

0
src/features/wifi/rapids/main.R → src/features/phone_wifi_connected/rapids/main.R
View File

46
src/features/phone_wifi_visible/rapids/main.R 100644
View File

@ -0,0 +1,46 @@
library(dplyr)
compute_wifi_feature <- function(data, feature, day_segment){
data <- data %>% filter_data_by_segment(day_segment)
if(feature %in% c("countscans", "uniquedevices")){
data <- data %>% group_by(local_segment)
data <- switch(feature,
"countscans" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n()),
"uniquedevices" = data %>% summarise(!!paste("wifi_rapids", feature, sep = "_") := n_distinct(bssid)))
return(data)
} else if(feature == "countscansmostuniquedevice"){
# Get the most scanned device
mostuniquedevice <- data %>%
group_by(bssid) %>%
mutate(N=n()) %>%
ungroup() %>%
filter(N == max(N)) %>%
head(1) %>% # if there are multiple device with the same amount of scans pick the first one only
pull(bssid)
return(data %>%
filter(bssid == mostuniquedevice) %>%
group_by(local_segment) %>%
summarise(!!paste("wifi_rapids", feature, sep = "_") := n()) %>%
replace(is.na(.), 0))
}
}
rapids_features <- function(sensor_data_files, day_segment, provider){
wifi_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
requested_features <- provider[["FEATURES"]]
# Output dataframe
features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
# The name of the features this function can compute
base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice")
# The subset of requested features this function can compute
features_to_compute <- intersect(base_features_names, requested_features)
for(feature_name in features_to_compute){
feature <- compute_wifi_feature(wifi_data, feature_name, day_segment)
features <- merge(features, feature, by="local_segment", all = TRUE)
}
return(features)
}

2
src/features/utils/resample_episodes.R
View File

@ -1,6 +1,8 @@
source("renv/activate.R")
library("dplyr")
library("tidyr")
library("tibble")
options(scipen=999)
# Using mostly indeixng instead of tidyr because is faster
resampled_episodes <- read.csv(snakemake@input[[1]])

2
src/features/utils/utils.py
View File

@ -74,7 +74,7 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
sensor_features = pd.DataFrame(columns=["local_segment"])
day_segments_labels = pd.read_csv(day_segments_file, header=0)
if "FEATURES" not in provider:
raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key))
raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key.upper()))
if provider["COMPUTE"] == True: